Blocking brain‐derived neurotrophic factor inhibits injury‐induced hyperexcitability of hippocampal <scp>CA</scp>3 neurons
Bibliographic record
Abstract
Brain trauma can disrupt synaptic connections, and this in turn can prompt axons to sprout and form new connections. If these new axonal connections are aberrant, hyperexcitability can result. It has been shown that ablating tropomyosin-related kinase B (TrkB), a receptor for brain-derived neurotrophic factor (BDNF), can reduce axonal sprouting after hippocampal injury. However, it is unknown whether inhibiting BDNF-mediated axonal sprouting will reduce hyperexcitability. Given this, our purpose here was to determine whether pharmacologically blocking BDNF inhibits hyperexcitability after injury-induced axonal sprouting in the hippocampus. To induce injury, we made Schaffer collateral lesions in organotypic hippocampal slice cultures. As reported by others, we observed a 50% reduction in axonal sprouting in cultures treated with a BDNF blocker (TrkB-Fc) 14 days after injury. Furthermore, lesioned cultures treated with TrkB-Fc were less hyperexcitable than lesioned untreated cultures. Using electrophysiology, we observed a two-fold decrease in the number of CA3 neurons that showed bursting responses after lesion with TrkB-Fc treatment, whereas we found no change in intrinsic neuronal firing properties. Finally, evoked field excitatory postsynaptic potential recordings indicated an increase in network activity within area CA3 after lesion, which was prevented with chronic TrkB-Fc treatment. Taken together, our results demonstrate that blocking BDNF attenuates injury-induced hyperexcitability of hippocampal CA3 neurons. Axonal sprouting has been found in patients with post-traumatic epilepsy. Therefore, our data suggest that blocking the BDNF-TrkB signaling cascade shortly after injury may be a potential therapeutic target for the treatment of post-traumatic epilepsy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".